code: def __init__(
      self,
      epochs: int = 100,
      batch_size: int = 32,
      num_batches_per_epoch: int = 50,
      learning_rate: float = 1e-3,
      weight_decay: float = 1e-6,
      maximum_learning_rate: float = 1e-2,
      clip_gradient: Optional[float] = None,
      device: Optional[Union[torch.device, str]] = None,
      **kwargs,
  ) -> None:
      self.epochs = epochs
      self.batch_size = batch_size
      self.num_batches_per_epoch = num_batches_per_epoch
      self.learning_rate = learning_rate
      self.weight_decay = weight_decay
      self.maximum_learning_rate = maximum_learning_rate
      self.clip_gradient = clip_gradient
      self.device = device
  
file_path: gluonts/src/gluonts/nursery/robust-mts-attack/pts/trainer.py
imports: import time
  from typing import List, Optional, Union
  from tqdm.auto import tqdm
  import torch
  import torch.nn
  from torch.optim import Adam
  from torch.optim.lr_scheduler import OneCycleLR
  from torch.utils.data import DataLoader
  from gluonts.core.component import validated
label_desc: Use image node_id 1 to create a new Trainer object  with example: obj = Trainer(epochs, batch_size, num_batches_per_epoch, learning_rate, weight_decay, maximum_learning_rate, clip_gradient, device)